Given the highlighted importance of context to capture figurative language phenomena and the difficulties of data annotation, transfer learning approaches are gaining attention in various domain adaptation problems. In particular, the utilization of pre-trained embeddings such as Global Vectors (GloVe) [1]}, and ELMo [2]} or leveraging Transformer seq2seq methods such as BERT (Bidirectional Encoder Representations from Transformers [3]}, RoBERTa [4]}, and XLNet [5]}, etc. are witnessing a surge.
